{
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   "id": "ff4d1ea2",
   "metadata": {},
   "outputs": [
    {
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     "output_type": "stream",
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      "Building wheels for collected packages: GPyOpt\n",
      "  Building wheel for GPyOpt (setup.py): started\n",
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      "Installing collected packages: libclang, flatbuffers, termcolor, tensorflow-io-gcs-filesystem, tensorflow-estimator, tensorboard-data-server, rsa, protobuf, opt-einsum, oauthlib, ml-dtypes, keras, grpcio, google-pasta, gast, cython, cachetools, astunparse, absl-py, requests-oauthlib, paramz, google-auth, GPy, google-auth-oauthlib, tensorboard, GPyOpt, tensorflow-intel, tensorflow\n",
      "Successfully installed GPy-1.13.1 GPyOpt-1.2.6 absl-py-2.1.0 astunparse-1.6.3 cachetools-5.3.3 cython-3.0.8 flatbuffers-23.5.26 gast-0.5.4 google-auth-2.28.1 google-auth-oauthlib-1.2.0 google-pasta-0.2.0 grpcio-1.62.0 keras-2.15.0 libclang-16.0.6 ml-dtypes-0.2.0 oauthlib-3.2.2 opt-einsum-3.3.0 paramz-0.9.6 protobuf-4.25.3 requests-oauthlib-1.3.1 rsa-4.9 tensorboard-2.15.2 tensorboard-data-server-0.7.2 tensorflow-2.15.0 tensorflow-estimator-2.15.0 tensorflow-intel-2.15.0 tensorflow-io-gcs-filesystem-0.31.0 termcolor-2.4.0\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "tables 3.8.0 requires blosc2~=2.0.0, which is not installed.\n"
     ]
    }
   ],
   "source": [
    "pip install pandas numpy scikit-learn tensorflow GPyOpt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ac7a1b02",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[65589 65589 65595 ... 64298 64266 64251]\n",
      "['22:48.3' '22:48.3' '22:48.3' ... '30:53.9' '30:53.9' '30:53.9']\n",
      "Is the signal bad? False\n",
      "Received parameters: [[5.00000000e+00 1.02400000e+03 4.15532469e-01 6.40000000e+01\n",
      "  2.83940973e-03]]\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "With n_samples=1, test_size=0.2 and train_size=None, the resulting train set will be empty. Adjust any of the aforementioned parameters.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[35], line 152\u001b[0m\n\u001b[0;32m    149\u001b[0m     plt\u001b[38;5;241m.\u001b[39mshow()\n\u001b[0;32m    151\u001b[0m \u001b[38;5;66;03m# Perform Bayesian optimization\u001b[39;00m\n\u001b[1;32m--> 152\u001b[0m optimizer \u001b[38;5;241m=\u001b[39m BayesianOptimization(f\u001b[38;5;241m=\u001b[39mobjective_function, domain\u001b[38;5;241m=\u001b[39msearch_space)\n\u001b[0;32m    153\u001b[0m optimizer\u001b[38;5;241m.\u001b[39mrun_optimization(max_iter\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m10\u001b[39m)\n\u001b[0;32m    154\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m************\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\GPyOpt\\methods\\bayesian_optimization.py:119\u001b[0m, in \u001b[0;36mBayesianOptimization.__init__\u001b[1;34m(self, f, domain, constraints, cost_withGradients, model_type, X, Y, initial_design_numdata, initial_design_type, acquisition_type, normalize_Y, exact_feval, acquisition_optimizer_type, model_update_interval, evaluator_type, batch_size, num_cores, verbosity, verbosity_model, maximize, de_duplication, **kwargs)\u001b[0m\n\u001b[0;32m    117\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minitial_design_type  \u001b[38;5;241m=\u001b[39m initial_design_type\n\u001b[0;32m    118\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minitial_design_numdata \u001b[38;5;241m=\u001b[39m initial_design_numdata\n\u001b[1;32m--> 119\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_init_design_chooser()\n\u001b[0;32m    121\u001b[0m \u001b[38;5;66;03m# --- CHOOSE the model type. If an instance of a GPyOpt model is passed (possibly user defined), it is used.\u001b[39;00m\n\u001b[0;32m    122\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel_type \u001b[38;5;241m=\u001b[39m model_type\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\GPyOpt\\methods\\bayesian_optimization.py:195\u001b[0m, in \u001b[0;36mBayesianOptimization._init_design_chooser\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    193\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mX \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    194\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mX \u001b[38;5;241m=\u001b[39m initial_design(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minitial_design_type, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mspace, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minitial_design_numdata)\n\u001b[1;32m--> 195\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mY, _ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobjective\u001b[38;5;241m.\u001b[39mevaluate(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mX)\n\u001b[0;32m    196\u001b[0m \u001b[38;5;66;03m# Case 2\u001b[39;00m\n\u001b[0;32m    197\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mX \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mY \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\GPyOpt\\core\\task\\objective.py:50\u001b[0m, in \u001b[0;36mSingleObjective.evaluate\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m     45\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m     46\u001b[0m \u001b[38;5;124;03mPerforms the evaluation of the objective at x.\u001b[39;00m\n\u001b[0;32m     47\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m     49\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_procs \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m---> 50\u001b[0m     f_evals, cost_evals \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_eval_func(x)\n\u001b[0;32m     51\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m     52\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\GPyOpt\\core\\task\\objective.py:74\u001b[0m, in \u001b[0;36mSingleObjective._eval_func\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m     72\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(x\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m]):\n\u001b[0;32m     73\u001b[0m     st_time    \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[1;32m---> 74\u001b[0m     rlt \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunc(np\u001b[38;5;241m.\u001b[39matleast_2d(x[i]))\n\u001b[0;32m     75\u001b[0m     f_evals     \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mvstack([f_evals,rlt])\n\u001b[0;32m     76\u001b[0m     cost_evals \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m [time\u001b[38;5;241m.\u001b[39mtime()\u001b[38;5;241m-\u001b[39mst_time]\n",
      "Cell \u001b[1;32mIn[35], line 94\u001b[0m, in \u001b[0;36mobjective_function\u001b[1;34m(params)\u001b[0m\n\u001b[0;32m     91\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1e6\u001b[39m\n\u001b[0;32m     93\u001b[0m \u001b[38;5;66;03m# Split indices into training and testing sets\u001b[39;00m\n\u001b[1;32m---> 94\u001b[0m bad_train_indices, bad_test_indices \u001b[38;5;241m=\u001b[39m train_test_split(bad_signal_indices, test_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.2\u001b[39m, random_state\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m42\u001b[39m)\n\u001b[0;32m     96\u001b[0m \u001b[38;5;66;03m# Check if bad_test_indices is not empty\u001b[39;00m\n\u001b[0;32m     97\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(bad_test_indices) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\_param_validation.py:211\u001b[0m, in \u001b[0;36mvalidate_params.<locals>.decorator.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    205\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m    206\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[0;32m    207\u001b[0m         skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[0;32m    208\u001b[0m             prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[0;32m    209\u001b[0m         )\n\u001b[0;32m    210\u001b[0m     ):\n\u001b[1;32m--> 211\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    212\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m InvalidParameterError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m    213\u001b[0m     \u001b[38;5;66;03m# When the function is just a wrapper around an estimator, we allow\u001b[39;00m\n\u001b[0;32m    214\u001b[0m     \u001b[38;5;66;03m# the function to delegate validation to the estimator, but we replace\u001b[39;00m\n\u001b[0;32m    215\u001b[0m     \u001b[38;5;66;03m# the name of the estimator by the name of the function in the error\u001b[39;00m\n\u001b[0;32m    216\u001b[0m     \u001b[38;5;66;03m# message to avoid confusion.\u001b[39;00m\n\u001b[0;32m    217\u001b[0m     msg \u001b[38;5;241m=\u001b[39m re\u001b[38;5;241m.\u001b[39msub(\n\u001b[0;32m    218\u001b[0m         \u001b[38;5;124mr\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparameter of \u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mw+ must be\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m    219\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparameter of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__qualname__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m must be\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m    220\u001b[0m         \u001b[38;5;28mstr\u001b[39m(e),\n\u001b[0;32m    221\u001b[0m     )\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\sklearn\\model_selection\\_split.py:2617\u001b[0m, in \u001b[0;36mtrain_test_split\u001b[1;34m(test_size, train_size, random_state, shuffle, stratify, *arrays)\u001b[0m\n\u001b[0;32m   2614\u001b[0m arrays \u001b[38;5;241m=\u001b[39m indexable(\u001b[38;5;241m*\u001b[39marrays)\n\u001b[0;32m   2616\u001b[0m n_samples \u001b[38;5;241m=\u001b[39m _num_samples(arrays[\u001b[38;5;241m0\u001b[39m])\n\u001b[1;32m-> 2617\u001b[0m n_train, n_test \u001b[38;5;241m=\u001b[39m _validate_shuffle_split(\n\u001b[0;32m   2618\u001b[0m     n_samples, test_size, train_size, default_test_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.25\u001b[39m\n\u001b[0;32m   2619\u001b[0m )\n\u001b[0;32m   2621\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m shuffle \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m:\n\u001b[0;32m   2622\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m stratify \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\sklearn\\model_selection\\_split.py:2273\u001b[0m, in \u001b[0;36m_validate_shuffle_split\u001b[1;34m(n_samples, test_size, train_size, default_test_size)\u001b[0m\n\u001b[0;32m   2270\u001b[0m n_train, n_test \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mint\u001b[39m(n_train), \u001b[38;5;28mint\u001b[39m(n_test)\n\u001b[0;32m   2272\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m n_train \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m-> 2273\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m   2274\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mWith n_samples=\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m, test_size=\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m and train_size=\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m, the \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   2275\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mresulting train set will be empty. Adjust any of the \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   2276\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124maforementioned parameters.\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(n_samples, test_size, train_size)\n\u001b[0;32m   2277\u001b[0m     )\n\u001b[0;32m   2279\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m n_train, n_test\n",
      "\u001b[1;31mValueError\u001b[0m: With n_samples=1, test_size=0.2 and train_size=None, the resulting train set will be empty. Adjust any of the aforementioned parameters."
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Conv1D, MaxPooling1D, Flatten, Dense, Dropout\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from GPyOpt.methods import BayesianOptimization\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import entropy, kurtosis\n",
    "# Load data from CSV\n",
    "file_path = 's10_run.csv'\n",
    "data = pd.read_csv(file_path,skiprows=0)# Skip zero rows if the first row contains headers\n",
    "\n",
    "# Extract PPG and time data\n",
    "ppg_data = data['ppg'].values\n",
    "time_data = data['timer'].values\n",
    "print(ppg_data)\n",
    "print(time_data)\n",
    "# Implement signal quality assessment conditions\n",
    "# (You need to implement the logic for BAD signal based on the conditions described)\n",
    "\n",
    "# Defining signal quality assessment conditions\n",
    "def assess_signal_quality(ppg_signal):\n",
    "    # Condition 1: Number of zero-crossings\n",
    "    zero_crossings = np.where(np.diff(np.sign(ppg_signal)))[0]\n",
    "    condition_1 = len(zero_crossings) > 2 * len(ppg_signal) / 1000  # Assuming 1000 samples per second\n",
    "\n",
    "    # Condition 2: Absolute value of normalized signal\n",
    "    normalized_signal = (ppg_signal - np.mean(ppg_signal)) / np.std(ppg_signal)\n",
    "    condition_2 = np.max(np.abs(normalized_signal)) > 2.58\n",
    "\n",
    "    # Condition 3: Entropy of the normalized signal\n",
    "    condition_3 = entropy(np.abs(normalized_signal)) > 5\n",
    "\n",
    "    # Condition 4: Kurtosis of the normalized frequency spectrum\n",
    "    spectrum = np.fft.fft(normalized_signal)\n",
    "    normalized_spectrum = np.abs(spectrum) / np.sum(np.abs(spectrum))\n",
    "    condition_4 = kurtosis(normalized_spectrum.real) > 2.4\n",
    "    if(condition_1=='True' or condition_2=='True'or condition_3 =='True' or condition_4=='True'):\n",
    "        return 'True'\n",
    "    else:\n",
    "        return 'False'\n",
    "\n",
    "# Example usage:\n",
    "#ppg_data_example = np.array([1, 8, 8, 3, 6, 5, 5])\n",
    "is_bad_signal = assess_signal_quality(ppg_data)\n",
    "print(\"Is the signal bad?\", is_bad_signal)\n",
    "\n",
    "# Implementing normalization, entropy calculation, and kurtosis calculation functions\n",
    "\n",
    "# Define the CNN model\n",
    "def build_model(num_cnn_layers, num_fc_nodes, dropout_rate, batch_size, learning_rate):\n",
    "    model = Sequential()\n",
    "    signal_length=7\n",
    "    model.add(Conv1D(filters=64, kernel_size=3, activation='relu', input_shape=(signal_length, 1)))\n",
    "    # Add more Conv1D layers based on num_cnn_layers\n",
    "    model.add(MaxPooling1D(pool_size=2))\n",
    "    model.add(Flatten())\n",
    "    model.add(Dense(num_fc_nodes, activation='relu'))\n",
    "    model.add(Dropout(dropout_rate))\n",
    "    model.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "    optimizer = Adam(learning_rate=learning_rate)\n",
    "    model.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "    return model\n",
    "# Define the objective function for Bayesian optimization\n",
    "# Modify the objective_function\n",
    "# Modify the objective_function\n",
    "# Modify the objective_function\n",
    "# Modify the objective_function\n",
    "# Modify the objective_function\n",
    "# Modify the objective_function\n",
    "def objective_function(params):\n",
    "    print(\"Received parameters:\", params)\n",
    "\n",
    "    num_cnn_layers = params[0][0]\n",
    "    num_fc_nodes = params[0][1]\n",
    "    dropout_rate = params[0][2]\n",
    "    batch_size = params[0][3]\n",
    "    learning_rate = params[0][4]\n",
    "\n",
    "    # Convert boolean array to indices of the positive class\n",
    "    bad_signal_indices = np.where(is_bad_signal)[0]\n",
    "\n",
    "    # Check if bad_signal_indices is not empty\n",
    "    if len(bad_signal_indices) == 0:\n",
    "        print(\"No bad signals found.\")\n",
    "        return -1e6\n",
    "\n",
    "    # Split indices into training and testing sets\n",
    "    bad_train_indices, bad_test_indices = train_test_split(bad_signal_indices, test_size=0.2, random_state=42)\n",
    "\n",
    "    # Check if bad_test_indices is not empty\n",
    "    if len(bad_test_indices) == 0:\n",
    "        print(\"No bad signals for testing.\")\n",
    "        return -1e6\n",
    "\n",
    "    # Preprocess data\n",
    "    scaler = StandardScaler()\n",
    "\n",
    "    # Train set\n",
    "    X_train = ppg_data[bad_train_indices].reshape(-1, 1)\n",
    "    y_train = is_bad_signal[bad_train_indices]\n",
    "    X_train_normalized = scaler.fit_transform(X_train).flatten()\n",
    "\n",
    "    # Test set\n",
    "    X_test = ppg_data[bad_test_indices].reshape(-1, 1)\n",
    "    y_test = is_bad_signal[bad_test_indices]\n",
    "    X_test_normalized = scaler.transform(X_test).flatten()\n",
    "\n",
    "    # Build and train the model\n",
    "    model = build_model(num_cnn_layers, num_fc_nodes, dropout_rate, batch_size, learning_rate)\n",
    "    try:\n",
    "        model.fit(X_train_normalized, y_train, epochs=10, batch_size=batch_size, verbose=0)\n",
    "\n",
    "        # Evaluate the model\n",
    "        y_pred = model.predict(X_test_normalized)\n",
    "        y_pred_class = np.round(y_pred)\n",
    "        accuracy = accuracy_score(y_test, y_pred_class)\n",
    "\n",
    "        return -accuracy  # Negative because Bayesian optimization aims to minimize the objective function\n",
    "    except Exception as e:\n",
    "        print(f\"Error during training: {e}\")\n",
    "        return -1e6\n",
    "\n",
    "# Modify the plotting function\n",
    "def plot_signal_with_color_variation(ppg_data, is_bad_signal):\n",
    "    # Create a time array based on the length of PPG data\n",
    "    time_array = np.arange(len(ppg_data))\n",
    "    # Plot the original signal\n",
    "    plt.figure(figsize=(10, 5))\n",
    "    plt.plot(time_array, ppg_data, label='Original Signal', color='yellow')\n",
    "\n",
    "    # Highlight BAD signals with color variation\n",
    "    bad_signal_indices = np.where(is_bad_signal)[0]\n",
    "    for bad_index in bad_signal_indices:\n",
    "        plt.axvspan(bad_index, bad_index + 1, facecolor='red', alpha=0.3, label='BAD Signal')\n",
    "\n",
    "    # Add labels and legend\n",
    "    plt.xlabel('Time')\n",
    "    plt.ylabel('PPG Signal')\n",
    "    plt.title('Original PPG Signal with BAD Signal Highlight')\n",
    "    plt.legend()\n",
    "\n",
    "    # Show the plot\n",
    "    plt.show()\n",
    "\n",
    "# Perform Bayesian optimization\n",
    "optimizer = BayesianOptimization(f=objective_function, domain=search_space)\n",
    "optimizer.run_optimization(max_iter=10)\n",
    "print('************')\n",
    "\n",
    "# Get the optimal hyperparameters\n",
    "optimal_params = optimizer.x_opt\n",
    "\n",
    "# Print the optimal hyperparameters\n",
    "print(\"Optimal Hyperparameters:\")\n",
    "print(f\"Number of CNN layers: {optimal_params[0]}\")\n",
    "print(f\"Number of fully connected nodes: {optimal_params[1]}\")\n",
    "print(f\"Dropout rate: {optimal_params[2]}\")\n",
    "print(f\"Batch size: {optimal_params[3]}\")\n",
    "print(f\"Learning rate: {optimal_params[4]}\")\n",
    "\n",
    "# Plot the result\n",
    "plot_signal_with_color_variation(ppg_data, is_bad_signal)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "7468d829",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hello world\n"
     ]
    }
   ],
   "source": [
    "print(\"hello world\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "2c47fec8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[65589 65589 65595 ... 64298 64266 64251]\n",
      "['22:48.3' '22:48.3' '22:48.3' ... '30:53.9' '30:53.9' '30:53.9']\n",
      "Is the signal bad? True\n",
      "Received parameters: [[6.000e+00 1.546e+03 2.000e-01 8.250e+02 2.000e-03]]\n",
      "Error during training: Expected int32 passed to parameter 'size' of op 'Slice', got [193875.0] of type 'list' instead. Error: Expected int32, but got 193875.0 of type 'float64'.\n",
      "Received parameters: [[6.000e+00 1.546e+03 2.000e-01 8.250e+02 2.000e-03]]\n",
      "Error during training: Expected int32 passed to parameter 'size' of op 'Slice', got [193875.0] of type 'list' instead. Error: Expected int32, but got 193875.0 of type 'float64'.\n",
      "Received parameters: [[6.000e+00 1.546e+03 2.000e-01 8.250e+02 2.000e-03]]\n",
      "Error during training: Expected int32 passed to parameter 'size' of op 'Slice', got [193875.0] of type 'list' instead. Error: Expected int32, but got 193875.0 of type 'float64'.\n",
      "Received parameters: [[6.000e+00 1.546e+03 2.000e-01 8.250e+02 2.000e-03]]\n",
      "Error during training: Expected int32 passed to parameter 'size' of op 'Slice', got [193875.0] of type 'list' instead. Error: Expected int32, but got 193875.0 of type 'float64'.\n",
      "Received parameters: [[6.000e+00 1.546e+03 2.000e-01 8.250e+02 2.000e-03]]\n",
      "Error during training: Expected int32 passed to parameter 'size' of op 'Slice', got [193875.0] of type 'list' instead. Error: Expected int32, but got 193875.0 of type 'float64'.\n",
      "************\n",
      "Optimal Hyperparameters:\n",
      "Number of CNN layers: 6.0\n",
      "Number of fully connected nodes: 1546.0\n",
      "Dropout rate: 0.2\n",
      "Batch size: 825.0\n",
      "Learning rate: 0.002\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Conv1D, MaxPooling1D, Flatten, Dense, Dropout\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from GPyOpt.methods import BayesianOptimization\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import entropy, kurtosis\n",
    "# Load data from CSV\n",
    "file_path = 's10_run.csv'\n",
    "data = pd.read_csv(file_path,skiprows=0)# Skip zero rows if the first row contains headers\n",
    "\n",
    "# Extract PPG and time data\n",
    "ppg_data = data['ppg'].values\n",
    "time_data = data['timer'].values\n",
    "print(ppg_data)\n",
    "print(time_data)\n",
    "# Implement signal quality assessment conditions\n",
    "# (You need to implement the logic for BAD signal based on the conditions described)\n",
    "\n",
    "# Defining signal quality assessment conditions\n",
    "def assess_signal_quality(ppg_signal):\n",
    "    # Condition 1: Number of zero-crossings\n",
    "    zero_crossings = np.where(np.diff(np.sign(ppg_signal)))[0]\n",
    "    condition_1 = len(zero_crossings) > 2 * len(ppg_signal) / 1000  # Assuming 1000 samples per second\n",
    "\n",
    "    # Condition 2: Absolute value of normalized signal\n",
    "    normalized_signal = (ppg_signal - np.mean(ppg_signal)) / np.std(ppg_signal)\n",
    "    condition_2 = np.max(np.abs(normalized_signal)) > 2.58\n",
    "\n",
    "    # Condition 3: Entropy of the normalized signal\n",
    "    condition_3 = entropy(np.abs(normalized_signal)) > 5\n",
    "\n",
    "    # Condition 4: Kurtosis of the normalized frequency spectrum\n",
    "    spectrum = np.fft.fft(normalized_signal)\n",
    "    normalized_spectrum = np.abs(spectrum) / np.sum(np.abs(spectrum))\n",
    "    condition_4 = kurtosis(normalized_spectrum.real) > 2.4\n",
    "\n",
    "    return any([condition_1, condition_2, condition_3, condition_4])\n",
    "\n",
    "# Example usage:\n",
    "#ppg_data_example = np.array([1, 8, 8, 3, 6, 5, 5])\n",
    "is_bad_signal = assess_signal_quality(ppg_data)\n",
    "print(\"Is the signal bad?\", is_bad_signal)\n",
    "\n",
    "# Implementing normalization, entropy calculation, and kurtosis calculation functions\n",
    "\n",
    "# Define the CNN model\n",
    "def build_model(num_cnn_layers, num_fc_nodes, dropout_rate, batch_size, learning_rate):\n",
    "    model = Sequential()\n",
    "    signal_length=7\n",
    "    model.add(Conv1D(filters=64, kernel_size=3, activation='relu', input_shape=(signal_length, 1)))\n",
    "    # Add more Conv1D layers based on num_cnn_layers\n",
    "    model.add(MaxPooling1D(pool_size=2))\n",
    "    model.add(Flatten())\n",
    "    model.add(Dense(num_fc_nodes, activation='relu'))\n",
    "    model.add(Dropout(dropout_rate))\n",
    "    model.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "    optimizer = Adam(learning_rate=learning_rate)\n",
    "    model.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "    return model\n",
    "# Define the objective function for Bayesian optimization\n",
    "def objective_function(params):\n",
    "    print(\"Received parameters:\", params)\n",
    "\n",
    "    num_cnn_layers = params[0][0]\n",
    "    num_fc_nodes = params[0][1]\n",
    "    dropout_rate = params[0][2]\n",
    "    batch_size = params[0][3]\n",
    "    learning_rate = params[0][4]\n",
    "\n",
    "    # Split data into training and testing sets\n",
    "    X_train, X_test, y_train, y_test = train_test_split(ppg_data, time_data, test_size=0.2, random_state=42)\n",
    "    \n",
    "    # Preprocess data\n",
    "    scaler = StandardScaler()\n",
    "    X_train_normalized = scaler.fit_transform(X_train.reshape(-1, 1)).flatten()\n",
    "    X_test_normalized = scaler.transform(X_test.reshape(-1, 1)).flatten()\n",
    "\n",
    "    # Build and train the model\n",
    "    model = build_model(num_cnn_layers, num_fc_nodes, dropout_rate, batch_size, learning_rate)\n",
    "    try:\n",
    "        model.fit(X_train_normalized, y_train, epochs=10, batch_size=batch_size, verbose=0)\n",
    "\n",
    "        # Evaluate the model\n",
    "        y_pred = model.predict(X_test_normalized)\n",
    "        y_pred_class = np.round(y_pred)\n",
    "        accuracy = accuracy_score(y_test, y_pred_class)\n",
    "    \n",
    "        return -accuracy  # Negative because Bayesian optimization aims to minimize the objective function\n",
    "    except Exception as e:\n",
    "        print(f\"Error during training: {e}\")\n",
    "        return -1e6\n",
    "\n",
    "\n",
    "# Define the search space for Bayesian optimization\n",
    "search_space = [\n",
    "    {'name': 'num_cnn_layers', 'type': 'discrete', 'domain': (6, 6)},\n",
    "    {'name': 'num_fc_nodes', 'type': 'discrete', 'domain': (1546, 1546)},\n",
    "    {'name': 'dropout_rate', 'type': 'continuous', 'domain': (0.2, 0.2)},\n",
    "    {'name': 'batch_size', 'type': 'discrete', 'domain': (825, 825)},\n",
    "    {'name': 'learning_rate', 'type': 'continuous', 'domain': (0.002, 0.002)}\n",
    "]\n",
    "# Perform Bayesian optimization\n",
    "optimizer = BayesianOptimization(f=objective_function, domain=search_space)\n",
    "optimizer.run_optimization(max_iter=10)\n",
    "print('************')\n",
    "\n",
    "# Get the optimal hyperparameters\n",
    "optimal_params = optimizer.x_opt\n",
    "\n",
    "# Print the optimal hyperparameters\n",
    "print(\"Optimal Hyperparameters:\")\n",
    "print(f\"Number of CNN layers: {optimal_params[0]}\")\n",
    "print(f\"Number of fully connected nodes: {optimal_params[1]}\")\n",
    "print(f\"Dropout rate: {optimal_params[2]}\")\n",
    "print(f\"Batch size: {optimal_params[3]}\")\n",
    "print(f\"Learning rate: {optimal_params[4]}\")\n",
    "\n",
    "# Plot the result\n",
    "# (You need to implement code to plot the original signal with color variation for BAD signals)\n",
    "\n",
    "# Show the plot\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Assuming 'ppg_data' is the original PPG signal and 'is_bad_signal' is a boolean indicating if the signal is BAD\n",
    "\n",
    "def plot_signal_with_color_variation(ppg_data, is_bad_signal):\n",
    "    # Create a time array based on the length of PPG data\n",
    "    time_array = np.arange(len(ppg_data))\n",
    "    # Plot the original signal\n",
    "    plt.figure(figsize=(10, 5))\n",
    "   # plt.xlim(0, 600)\n",
    "    plt.plot(time_array, ppg_data, label='Original Signal', color='yellow')\n",
    "\n",
    "    # Highlight BAD signals with color variation\n",
    "    bad_signal_indices = np.where(is_bad_signal)[0]\n",
    "    for bad_index in bad_signal_indices:\n",
    "        plt.axvspan(bad_index, bad_index + 1, facecolor='red', alpha=0.3, label='BAD Signal')\n",
    "\n",
    "    # Add labels and legend\n",
    "    plt.xlabel('Time')\n",
    "    plt.ylabel('PPG Signal')\n",
    "    plt.title('Original PPG Signal with BAD Signal Highlight')\n",
    "    plt.legend()\n",
    "\n",
    "    # Show the plot\n",
    "    plt.show()\n",
    "\n",
    "# Example usage:\n",
    "# Assuming 'is_bad_signal' is a boolean array indicating if the corresponding signal is BAD\n",
    "is_bad_signal = np.array([True, True, False, True, True, True, True])\n",
    "plot_signal_with_color_variation(ppg_data, is_bad_signal)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "edbe820d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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